Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- 1 Intelligence in Biological Systems
- 2 Principles of Artificial Neural Networks
- 3 Artificial Synapses
- 4 Artificial Neurons
- 5 Examples of Applications in Artificial Neural Networks
- 6 System Design
- 7 Neuromorphic Algorithms
- 8 Lifelong Learning with AI Algorithms and Hardware
- 9 Practice Problems
- Index
- References
1 - Intelligence in Biological Systems
Published online by Cambridge University Press: 01 January 2026
Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- 1 Intelligence in Biological Systems
- 2 Principles of Artificial Neural Networks
- 3 Artificial Synapses
- 4 Artificial Neurons
- 5 Examples of Applications in Artificial Neural Networks
- 6 System Design
- 7 Neuromorphic Algorithms
- 8 Lifelong Learning with AI Algorithms and Hardware
- 9 Practice Problems
- Index
- References
Summary
The chapter begins with discussion of intelligence in simple unicellular organisms followed by that of animals with complex nervous systems. Surprisingly, even organisms that do not have a central brain can navigate their complex environments, forage, and learn. In organisms with central nervous system, neurons and synapses in the brain provide elementary basis of intelligence and memory. Neurons generate action potentials that represent information. Synapses hold memory and control the signal transmission between neurons. A key feature of biological neural circuits is plasticity, that is, their ability to modify the circuit properties based both on stimuli and time intervals between them. This represents one form of learning. The biological brain is not static but continuously evolves based on the experience. The field of AI seeks to learn from biological neural circuitry, emulate aspects of intelligence and learning and attempts to build physical devices and algorithms that can demonstrate features of animal intelligence. Neuromorphic computing therefore requires a paradigm shift in design of semiconductors as well as algorithm foundations that are not necessarily built for perfection, rather for learning.
Keywords
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- Introduction to Neuromorphic Computing , pp. 1 - 14Publisher: Cambridge University PressPrint publication year: 2026
References
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